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      "source": [
        "from torchvision import datasets\n",
        "import torch\n",
        "data_folder = '/content/' # This can be any directory you want to download FMNIST to\n",
        "fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ],
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          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ],
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          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ],
          "name": "stdout"
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        {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "Extracting /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
            "Processing...\n",
            "Done!\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Upbh6zarK2d5"
      },
      "source": [
        "tr_images = fmnist.data\n",
        "tr_targets = fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZBVdvanK34A"
      },
      "source": [
        "val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
        "val_images = val_fmnist.data\n",
        "val_targets = val_fmnist.targets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkis84DbK5YH"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jP9feXa2zDUn"
      },
      "source": [
        "### Dropout 0.25"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2dYPVTxK7zf"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Dropout(0.25),\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Dropout(0.25),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    model.train()\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvEH2ZnKLCUf"
      },
      "source": [
        "def get_data():     \n",
        "    train = FMNISTDataset(tr_images, tr_targets)     \n",
        "    trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
        "    val = FMNISTDataset(val_images, val_targets)     \n",
        "    val_dl = DataLoader(val, batch_size=len(val_images), shuffle=True)\n",
        "    return trn_dl, val_dl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XOV367XiLD8c"
      },
      "source": [
        "@torch.no_grad()\n",
        "def val_loss(x, y, model):\n",
        "    model.eval()\n",
        "    prediction = model(x)\n",
        "    val_loss = loss_fn(prediction, y)\n",
        "    return val_loss.item()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPs8i2C5LFNT"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wm0SC1giLGYk",
        "outputId": "9ab707e7-e5ec-4914-bb9a-b76d20cb413b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(30):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model)\n",
        "        validation_loss = val_loss(x, y, model)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n",
            "14\n",
            "15\n",
            "16\n",
            "17\n",
            "18\n",
            "19\n",
            "20\n",
            "21\n",
            "22\n",
            "23\n",
            "24\n",
            "25\n",
            "26\n",
            "27\n",
            "28\n",
            "29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXFV4oPSLI76",
        "outputId": "e370cea2-4bc7-469c-8459-e0a4c92c3d0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for ix, par in enumerate(model.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-XKZdlYDITb",
        "outputId": "53d3618b-5700-4540-b360-6b222b6eeed8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(30)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss with dropout')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy with dropout')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "#plt.ylim(0.8,1)\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y0pre4AfDIWE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPyLGbaYMH0T"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ywRhABUqMTXZ"
      },
      "source": [
        "### No Dropout"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VfThcoVJMTZ-"
      },
      "source": [
        "class FMNISTDataset(Dataset):\n",
        "    def __init__(self, x, y):\n",
        "        x = x.float()/255\n",
        "        x = x.view(-1,28*28)\n",
        "        self.x, self.y = x, y \n",
        "    def __getitem__(self, ix):\n",
        "        x, y = self.x[ix], self.y[ix]        \n",
        "        return x.to(device), y.to(device)\n",
        "    def __len__(self): \n",
        "        return len(self.x)\n",
        "\n",
        "from torch.optim import SGD, Adam\n",
        "def get_model():\n",
        "    model = nn.Sequential(\n",
        "        nn.Linear(28 * 28, 1000),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(1000, 10)\n",
        "    ).to(device)\n",
        "\n",
        "    loss_fn = nn.CrossEntropyLoss()\n",
        "    optimizer = Adam(model.parameters(), lr=1e-3)\n",
        "    return model, loss_fn, optimizer\n",
        "\n",
        "def train_batch(x, y, model, opt, loss_fn):\n",
        "    prediction = model(x)\n",
        "    batch_loss = loss_fn(prediction, y)\n",
        "    batch_loss.backward()\n",
        "    optimizer.step()\n",
        "    optimizer.zero_grad()\n",
        "    return batch_loss.item()\n",
        "\n",
        "def accuracy(x, y, model):\n",
        "    with torch.no_grad():\n",
        "        prediction = model(x)\n",
        "    max_values, argmaxes = prediction.max(-1)\n",
        "    is_correct = argmaxes == y\n",
        "    return is_correct.cpu().numpy().tolist()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srZdvgL8MUWP"
      },
      "source": [
        "trn_dl, val_dl = get_data()\n",
        "model2, loss_fn, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44QWT2kgMWN7",
        "outputId": "0fc4cc98-5aec-4ec9-e4ae-0dc35d0cdab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "source": [
        "train_losses, train_accuracies = [], []\n",
        "val_losses, val_accuracies = [], []\n",
        "for epoch in range(30):\n",
        "    print(epoch)\n",
        "    train_epoch_losses, train_epoch_accuracies = [], []\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        batch_loss = train_batch(x, y, model2, optimizer, loss_fn)\n",
        "        train_epoch_losses.append(batch_loss)        \n",
        "    train_epoch_loss = np.array(train_epoch_losses).mean()\n",
        "\n",
        "    for ix, batch in enumerate(iter(trn_dl)):\n",
        "        x, y = batch\n",
        "        is_correct = accuracy(x, y, model2)\n",
        "        train_epoch_accuracies.extend(is_correct)\n",
        "    train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
        "\n",
        "    for ix, batch in enumerate(iter(val_dl)):\n",
        "        x, y = batch\n",
        "        val_is_correct = accuracy(x, y, model2)\n",
        "        validation_loss = val_loss(x, y, model2)\n",
        "    val_epoch_accuracy = np.mean(val_is_correct)\n",
        "\n",
        "    train_losses.append(train_epoch_loss)\n",
        "    train_accuracies.append(train_epoch_accuracy)\n",
        "    val_losses.append(validation_loss)\n",
        "    val_accuracies.append(val_epoch_accuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n",
            "13\n",
            "14\n",
            "15\n",
            "16\n",
            "17\n",
            "18\n",
            "19\n",
            "20\n",
            "21\n",
            "22\n",
            "23\n",
            "24\n",
            "25\n",
            "26\n",
            "27\n",
            "28\n",
            "29\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Yg4eyCQMYJO",
        "outputId": "e625b1c5-84dd-486b-91c9-4bb720c1df48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for ix, par in enumerate(model2.parameters()):\n",
        "  if(ix==0):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting input to hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix ==1):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of hidden layer')\n",
        "      plt.show()\n",
        "  elif(ix==2):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of weights conencting hidden to output layer')\n",
        "      plt.show()\n",
        "  elif(ix ==3):\n",
        "      plt.hist(par.cpu().detach().numpy().flatten())\n",
        "      #plt.xlim(-2,2)\n",
        "      plt.title('Distribution of biases of output layer')\n",
        "      plt.show()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KbW8sfzcBFwMLevF4ERFR36iDXtIc4MvAm2zf0lL+FElTB6aBI4GOZ+5ERMTY6Tp0I+k84DBguqR+4FRgJwDbZwCnAE8HPisJYGs5w2ZP4OJStiPwRdtfG4M+RETEEOqcdXN0l/rjgeM7lG8ADvzdJSIiYjzlm7EREQ2XoI+IaLgEfUREwyXoIyIaLkEfEdFwCfqIiIZL0EdENFyCPiKi4RL0ERENl6CPiGi4BH1ERMMl6CMiGi5BHxHRcAn6iIiGS9BHRDRcgj4iouES9BERDVcr6CWtkLRJUsdrvqryaUnrJV0n6QUtdUsk3VpuS3rV8IiIqKfuHv3ZwMIh6l8JzC+3pcC/AEjag+oas4cAC4BTJU0baWMjImL4agW97SuBLUPMsgg4x5U1wO6S9gJeAay2vcX2vcBqhn7DiIiIHut6cfCaZgJ3tNzvL2WDlf8OSUupPg0wZ86cETdk3omXjnjZiMlqe3xdb499vv2jrx6T9U6ag7G2l9vus903Y8aMiW5ORERj9CroNwKzW+7PKmWDlUdExDjpVdCvBN5czr55MXC/7buAy4AjJU0rB2GPLGURETFOao3RSzoPOAyYLqmf6kyanQBsnwGsAl4FrAceAv6i1G2RdBqwtqxqme2hDupGRESP1Qp620d3qTfw9kHqVgArht+0iIjohUlzMDYiIsZGgj4iouES9BERDZegj4houAR9RETDJegjIhouQR8R0XAJ+oiIhkvQR0Q0XII+IqLhEvQREQ2XoI+IaLgEfUREwyXoIyIaLkEfEdFwCfqIiIZL0EdENFytoJe0UNLNktZLOrFD/ackXVtut0i6r6Xu8Za6lb1sfEREdNf1UoKSpgCnA0cA/cBaSStt3zgwj+13tcz/DuDgllU8bPug3jU5IiKGo84e/QJgve0Nth8FzgcWDTH/0cB5vWhcRESMXp2gnwnc0XK/v5T9Dklzgb2Bb7YU7yJpnaQ1kl472INIWlrmW7d58+YazYqIiDp6fTB2MXCh7cdbyuba7gP+HPgnSf+j04K2l9vus903Y8aMHjcrImL7VSfoNwKzW+7PKmWdLKZt2Mb2xvJ3A3AF247fR0TEGKsT9GuB+ZL2lrQzVZj/ztkzkp4DTAO+21I2TdKTyvR04CXAje3LRkTE2Ol61o3trZJOAC4DpgArbN8gaRmwzvZA6C8GzrftlsX3A86U9ATVm8pHW8/WiYiIsdc16AFsrwJWtZWd0nb/Qx2Wuwp43ijaFxERo5RvxkZENFyCPiKi4RL0ERENl6CPiGi4BH1ERMMl6CMiGi5BHxHRcAn6iIiGS9BHRDRcgj4iouES9BERDZegj4houAR9RETDJegjIhouQR8R0XAJ+oiIhkvQR0Q0XK2gl7RQ0s2S1ks6sUP9sZI2S7q23I5vqVsi6dZyW9LLxkdERHddLyUoaQpwOnAE0A+slbSyw7VfL7B9QtuyewCnAn2AgWvKsvf2pPUREdFVnT36BcB62xtsPwqcDyyquf5XAKttbynhvhpYOLKmRkTESNQJ+pnAHS33+0tZu9dJuk7ShZJmD3NZJC2VtE7Sus2bN9doVkRE1NGrg7H/Acyz/XyqvfbPD3cFtpfb7rPdN2PGjB41KyIi6gT9RmB2y/1Zpew3bN9j+5Fy9yzghXWXjYiIsVUn6NcC8yXtLWlnYDGwsnUGSXu13D0KuKlMXwYcKWmapGnAkaUsIiLGSdezbmxvlXQCVUBPAVbYvkHSMmCd7ZXAOyUdBWwFtgDHlmW3SDqN6s0CYJntLWPQj4iIGETXoAewvQpY1VZ2Ssv0ScBJgyy7AlgxijZGRMQo5JuxERENl6CPiGi4BH1ERMMl6CMiGi5BHxHRcAn6iIiGS9BHRDRcgj4iouES9BERDZegj4houAR9RETDJegjIhouQR8R0XAJ+oiIhkvQR0Q0XII+IqLhEvQREQ1XK+glLZR0s6T1kk7sUP9uSTdKuk7SNyTNbal7XNK15bayfdmIiBhbXS8lKGkKcDpwBNAPrJW00vaNLbP9AOiz/ZCktwL/CLyh1D1s+6AetzsiImqqs0e/AFhve4PtR4HzgUWtM9j+lu2Hyt01wKzeNjMiIkaqTtDPBO5oud9fygZzHPDVlvu7SFonaY2k1w62kKSlZb51mzdvrtGsiIioo+vQzXBIOgboAw5tKZ5re6OkfYBvSrre9k/al7W9HFgO0NfX5162KyJie1Znj34jMLvl/qxStg1JhwMnA0fZfmSg3PbG8ncDcAVw8CjaGxERw1Qn6NcC8yXtLWlnYDGwzdkzkg4GzqQK+U0t5dMkPalMTwdeArQexI2IiDHWdejG9lZJJwCXAVOAFbZvkLQMWGd7JfBx4KnAlyQB/Mz2UcB+wJmSnqB6U/lo29k6ERExxmqN0dteBaxqKzulZfrwQZa7CnjeaBoYERGjk2/GRkQ0XII+IqLhEvQREQ2XoI+IaLgEfUREwyXoIyIaLkEfEdFwCfqIiIZL0EdENFyCPiKi4RL0ERENl6CPiGi4BH1ERMMl6CMiGi5BHxHRcAn6iIiGS9BHRDRcraCXtFDSzZLWSzqxQ/2TJF1Q6r8naV5L3Uml/GZJr+hd0yMioo6uQS9pCnA68Epgf+BoSfu3zXYccK/tZwOfAj5Wlt2f6mLizwUWAp8t64uIiHFSZ49+AbDe9gbbjwLnA4va5lkEfL5MXwj8iaqrhC8Czrf9iO3bgPVlfRERMU7qXBx8JnBHy/1+4JDB5rG9VdL9wNNL+Zq2ZWd2ehBJS4Gl5e6Dkm6u0bZemA7cPU6PNSb0MaAB/eD3oA9lW3cz6ftRQ/owAQZ5fdXtx9zBKuoE/biwvRxYPt6PK2md7b7xftxea0I/mtAHaEY/0ofJoxf9qDN0sxGY3XJ/VinrOI+kHYGnAffUXDYiIsZQnaBfC8yXtLeknakOrq5sm2clsKRM/xnwTdsu5YvLWTl7A/OBq3vT9IiIqKPr0E0Zcz8BuAyYAqywfYOkZcA62yuBzwHnSloPbKF6M6DM9+/AjcBW4O22Hx+jvozUuA8XjZEm9KMJfYBm9CN9mDxG3Q9VO94REdFU+WZsRETDJegjIhpuuwt6SXtIWi3p1vJ32iDzzZF0uaSbJN3Y+rMOk0HdfpR5d5PUL+kz49nGbur0QdJBkr4r6QZJ10l6w0S0td1ofhZkMqnRj3eX1/91kr4hadBztSdKtz60zPc6SZY0KU+5rNMPSa8vz8cNkr5Ye+W2t6sb8I/AiWX6ROBjg8x3BXBEmX4qsOtEt30k/Sj1/wx8EfjMRLd7uH0A9gXml+lnAXcBu09wu6cAPwH2AXYGfgjs3zbP24AzyvRi4IKJ3t4j7MfLB177wFsnWz/q9KHMNxW4kuoLnH0T3e4RPhfzgR8A08r9Z9Rd/3a3R8+2P9fweeC17TOU3+jZ0fZqANsP2n5o/JpYS9d+AEh6IbAncPk4tWs4uvbB9i22by3TdwKbgBnj1sLORvOzIJNJ137Y/lbLa38N1XdhJpM6zwXAaVS/wfXr8WzcMNTpx18Bp9u+F8D2pror3x6Dfk/bd5Xpn1OFYLt9gfskfVnSDyR9fBL+GFvXfkjaAfgE8N7xbNgw1HkufkPSAqq9nZ+MdcO66PSzIO0/7bHNz4IAAz8LMpnU6Uer44CvjmmLhq9rHyS9AJht+9LxbNgw1Xku9gX2lfTfktZIWlh35ZPmJxB6SdLXgWd2qDq59Y5tS+p0fumOwMuAg4GfARcAx1J9X2Dc9KAfbwNW2e6fqJ3JHvRhYD17AecCS2w/0dtWRjeSjgH6gEMnui3DUXZ2Pkn1//v7bkeq4ZvDqD5ZXSnpebbvq7Ng49g+fLA6Sb+QtJftu0p4dPr40w9ca3tDWeYS4MWMc9D3oB9/CLxM0tuojjPsLOlB24MesOq1HvQBSbsBlwIn217TaZ5xNpyfBelv+1mQyaTWT5RIOpzqjflQ24+MU9vq6taHqcABwBVlZ+eZwEpJR9leN26t7K7Oc9EPfM/2Y8Btkm6hCv613Va+PQ7dtP5cwxLgKx3mWQvsLmlgLPiPqb7dO5l07YftN9qeY3se1fDNOeMZ8jV07UP52Y2Lqdp+4Ti2bSij+VmQyaRrPyQdDJwJHDWcMeFxNGQfbN9ve7rteeX/YA1VXyZTyEO919QlVHvzSJpONZSzodbaJ/po83jfqMZJvwHcCnwd2KOU9wFntcx3BHAdcD1wNrDzRLd9JP1omf9YJt9ZN137ABwDPAZc23I7aBK0/VXALVTHC04uZcuoQgRgF+BLVNdguBrYZ6LbPMJ+fB34Rcu2XznRbR5uH9rmvYJJeNZNzedCVMNQN5ZcWlx33fkJhIiIhtseh24iIrYrCfqIiIZL0EdENFyCPiKi4RL0ERENl6CPiGi4BH1ERMP9f1xsnH+dZBoUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkRVIA97M0iN",
        "outputId": "6573270f-0923-4a38-eb1f-7b03225dddc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "epochs = np.arange(30)+1\n",
        "import matplotlib.ticker as mtick\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "%matplotlib inline\n",
        "plt.subplot(211)\n",
        "plt.plot(epochs, train_losses, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_losses, 'r', label='Validation loss')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation loss without dropout')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()\n",
        "plt.subplot(212)\n",
        "plt.plot(epochs, train_accuracies, 'bo', label='Training accuracy')\n",
        "plt.plot(epochs, val_accuracies, 'r', label='Validation accuracy')\n",
        "#plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))\n",
        "plt.title('Training and validation accuracy without dropout')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "#plt.ylim(0.8,1)\n",
        "plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
        "plt.legend()\n",
        "plt.grid('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwSHnFH7zDVP"
      },
      "source": [
        "###  Weight distribution per layer - Model with no dropout"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pntsEc5Gxbd",
        "outputId": "0eb3e5b4-ce68-4c4e-ffe7-a13fb6d5ad7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for par in model2.parameters():\n",
        "  plt.hist(par.cpu().detach().numpy().flatten())\n",
        "  plt.xlim(-2,2)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjoiV2tizDVS"
      },
      "source": [
        "###  Weight distribution per layer - Model with dropout"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lgB3eu2HAKI",
        "outputId": "5085602e-7e76-4840-ca71-8736c87216a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for par in model.parameters():\n",
        "  plt.hist(par.cpu().detach().numpy().flatten())\n",
        "  plt.xlim(-2,2)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBxoI77_S-Tj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}